I think we have a hard time disconnecting our personal experience with the observations we make. When we look at a photograph of a person riding a bicycle down

I think we have a hard time disconnecting our personal experience with the observations we make. When we look at a photograph of a person riding a bicycle down a path, even if we've never ridden a bicycle we've likely been outdoors, stood on a path, felt wind when we moved, etc. We may not be able to accurately simulate the experience in our mind but we can get close. On the other hand, the starting point for an ML system interpreting that same image is essentially a stream of scalar values that tend to demonstrate multiple layers of periodicity (3-4 byte intervals for RGBA and then another layer per line of rasterized image data and yet another per frame if it's a video). Here's a quick experiment. Let the video linked below play for a five count (sound is essential but a bit intense so maybe moderate the volume first) so you have some confidence it's not just me playing a rude trick, then close your eyes for a five count. There's going to be a major change in the sound when you get near 'five', now try to imagine how the scene changed before opening your eyes again: https://youtu.be/qnL40CbuodU?t=25 https://youtu.be/qnL40CbuodU?t=25 I think without any reference from an embodied perspective, we're asking ML systems to understand the sounds (which are also streams of scalar values that demonstrate periodicity) the same way we interpret the representation visually. (Also if you enjoyed the example above check out these two channels, some of them are mindblowing) https://www.youtube.com/user/jerobeamfenderson1 https://www.youtube.com/user/jerobeamfenderson1 https://www.youtube.com/c/ChrisAllenMusic https://www.youtube.com/c/ChrisAllenMusic And a fun video explaining it all - https://www.youtube.com/watch?v=4gibcRfp4zA https://www.youtube.com/watch?v=4gibcRfp4zA